1. Technical Field
The present disclosure relates to breast MRI and, more specifically, to multi-scale analysis of signal enhancement in breast MRI.
2. Discussion of Related Art
Computer aided diagnosis (CAD) is the process of using computer vision systems to analyze medical image data and make a determination as to what regions of the image data are potentially problematic. Some CAD techniques then present these regions of suspicion to a medical professional such as a radiologist for manual review, while other CAD techniques make a preliminary determination as to the nature of the region of suspicion. For example, some CAD techniques may characterize each region of suspicion as a lesion or a non-lesion. The final results of the CAD system may then be used by the medical professional to aid in rendering a final diagnosis.
Because CAD techniques may identify lesions that may have been overlooked by a medical professional working without the aid of a CAD system, and because CAD systems can quickly direct the focus of a medical professional to the regions most likely to be of diagnostic interest, CAD systems may be highly effective in increasing the accuracy of a diagnosis and decreasing the time needed to render diagnosis. Accordingly, scarce medical resources may be used to benefit a greater number of patients with high efficiency and accuracy.
CAD techniques have been applied to the field of mammography, where low-dose x-rays are used to image a patient's breast to diagnose suspicious breast lesions. However, because mammography relies on x-ray imaging, mammography may expose a patient to potentially harmful ionizing radiation. As many patients are instructed to undergo mammography on a regular basis, the administered ionizing radiation may, over time, pose a risk to the patient. Moreover, it may be difficult to use x-rays to differentiate between different forms of masses that may be present in the patient's breast. For example, it may be difficult to distinguish between calcifications and malignant lesions.
Magnetic resonance imaging (MRI) is a medical imaging technique that uses a powerful magnetic field to image the internal structure and certain functionality of the human body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of lesions.
In dynamic contrast enhanced MRI (DCE-MRI), many additional details pertaining to bodily soft tissue may be observed. These details may be used to further aid in diagnosis and treatment of detected lesions.
DCE-MRI may be performed by acquiring a sequence of MR images that span a time before magnetic contrast agents are introduced into the patient's body and a time after the magnetic contrast agents are introduced. For example, a first MR image may be acquired prior to the introduction of the magnetic contrast agents, and subsequent MR images may be taken at a rate of one image per minute for a desired length of time. By imaging the body in this way, a set of images may be acquired that illustrate how the magnetic contrast agent is absorbed and washed out from various portions of the patient's body. This absorption and washout information may be used to characterize various internal structures within the body and may provide additional diagnostic information.
In DCE-MRI, the acquired sequence of MR images may be combined by subtracting images from different time periods to determine how the distribution of the magnetic contrast agent has changed from image to image. However, the nature of this subtraction process is such that the resulting images may be highly susceptible to noise. This may be at least in part the result of a high-pass filter effect that is caused by the subtraction process.
The presence of this subtraction noise may be so pronounced that even portions of the MR images that include only air that surrounds the subject during image acquisition may include areas of enhancement as well as areas that appear to be small vessels and/or scars.
Accordingly, to reduce the presence of subtraction noise, one or more noise reduction techniques may be applied to the subtracted images before CAD may be performed on the image data. Examples of such noise reduction techniques include morphological operators such as those discussed in J. Serra, “Image Analysis and Mathematical Morphology”, Academic Press, 1982, which is incorporated by reference, as well as various smoothing filters such as T. Lindinberg, “Scale-Space Theory in Computer Vision”, Kluwer Academic Publishers, 1994, which is also incorporated by reference.
Noise reduction using morphological operators typically calculates a set of inscribed spheres at interior voxels of a region of interest and eliminates all voxels that fall outside of the spheres. Smoothing filters typically involve convolution of an image with a Gaussian kernel e(−(x2+y2)/2σ)/2πσ to blur the image.
While these noise reduction approaches, and similar approaches, may be effective for many purposes, when applied to the automatic detection of breast lesions from MR images, the application of these noise reduction techniques may lead to often subtle but sometimes drastic changes in the shape and/or texture of detected lesions. Because characterization of a lesion as benign or potentially malignant often involves analyzing the smoothness of the margin of the lesion, conventional techniques for reducing noise in medical images may inadvertently remove the subtle shape and/or texture information from the margin of the lesion that is important in characterizing the lesion as benign or potentially malignant.
Accordingly, existing techniques for the reduction of subtraction noise in a sequence of medical images may be ill suited for application to automatic detection of breast lesions from MR images.